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Learning-To-Rank

Learning to rank is the application of machine learning to build ranking models. Some common use cases for ranking models are information retrieval (e.g., web search) and news feeds application (think Twitter, Facebook, Instagram).

Papers

Showing 151200 of 753 papers

TitleStatusHype
LaSER: Language-Specific Event RecommendationCode0
Learning a Deep Listwise Context Model for Ranking RefinementCode0
DCM Bandits: Learning to Rank with Multiple ClicksCode0
Learning to Rank Aspects and Opinions for Comparative ExplanationsCode0
Joint Representation Learning for Top-N Recommendation with Heterogeneous Information SourcesCode0
Learning to rank for censored survival dataCode0
Is Non-IID Data a Threat in Federated Online Learning to Rank?Code0
Is Interpretable Machine Learning Effective at Feature Selection for Neural Learning-to-Rank?Code0
Joint Optimization of Cascade Ranking ModelsCode0
Differentiable Unbiased Online Learning to RankCode0
Learning to Rank Rationales for Explainable RecommendationCode0
MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph FusionCode0
Learning to Explain Entity Relationships in Knowledge GraphsCode0
Distilled Neural Networks for Efficient Learning to RankCode0
Distractor Generation for Multiple Choice Questions Using Learning to RankCode0
Balancing Speed and Quality in Online Learning to Rank for Information RetrievalCode0
Learning to Rank Query Graphs for Complex Question Answering over Knowledge GraphsCode0
ImitAL: Learning Active Learning Strategies from Synthetic DataCode0
Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to RankCode0
ImitAL: Learned Active Learning Strategy on Synthetic DataCode0
Improving Pairwise Ranking for Multi-label Image ClassificationCode0
Duet at TREC 2019 Deep Learning TrackCode0
BEER 1.1: ILLC UvA submission to metrics and tuning taskCode0
Match-Tensor: a Deep Relevance Model for SearchCode0
Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to RankCode0
Counterfactual Learning to Rank using Heterogeneous Treatment Effect EstimationCode0
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading BanditsCode0
Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to RankCode0
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVMCode0
Hidden or Inferred: Fair Learning-To-Rank with Unknown DemographicsCode0
Hashing as Tie-Aware Learning to RankCode0
Mend The Learning Approach, Not the Data: Insights for Ranking E-Commerce ProductsCode0
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
Groupwise Query Performance Prediction with BERTCode0
On Curriculum Learning for Commonsense ReasoningCode0
HAPI: A Model for Learning Robot Facial Expressions from Human PreferencesCode0
How to Forget Clients in Federated Online Learning to Rank?Code0
End-to-End Neural Ad-hoc Ranking with Kernel PoolingCode0
Intersection of Parallels as an Early Stopping CriterionCode0
Overcoming Prior Misspecification in Online Learning to RankCode0
CoSPLADE: Contextualizing SPLADE for Conversational Information RetrievalCode0
Fitting Sentence Level Translation Evaluation with Many Dense FeaturesCode0
FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social FeedsCode0
Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a DocumentCode0
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue SystemsCode0
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted TreesCode0
Contextual Semibandits via Supervised Learning OraclesCode0
Exact Passive-Aggressive Algorithms for Learning to Rank Using Interval LabelsCode0
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to RankCode0
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMsCode0
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